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A Profit Maximization Scheme With Guaranteed Quality Of Service

In Cloud Computing

1.Gosikonda Neeraja 2. Katukoori Shekhar 3.V Janaki

1.PG Scholar, Department of CSE, Vaagdevi College Of Engineering Bollikunta,Warangal, Telangana Mail ID : neeraja.nvs07@gmail.com

2. Assistant Professor Department of CSE, Vaagdevi College of Engineering, Bollikunta, Warangal ,Telangana Mail ID : .shekhar.ktk@gmail.com

3. Professor ,HOD Department of CSE, Vaagdevi College of Engineering,Bollikunta, Warangal ,Telangana

Abstract:

An effective and efficient way to provide

computing resources and services to customers

on demand, cloud computing has become more

and more popular. From cloud service

providers’ perspective, profit is one of the most

important considerations, and it is mainly

determined by the configuration of a cloud

service platform under given market demand.

However, a single long-term renting scheme is

usually adopted to configure a cloud platform,

which cannot guarantee the service quality but

leads to serious resource waste. In this paper, a

double resource renting scheme is designed

firstly in which short-term renting and long-term

renting are combined aiming at the existing

issues. This double renting scheme can

effectively guarantee the quality of service of all

requests and reduce the resource waste greatly.

Secondly, a service system is considered as an

M/M/m+D queuing model and the performance

indicators that affect the profit of our double

renting scheme are analyzed, e.g., the average

charge, the ratio of requests that need temporary

servers, and so forth. Thirdly, a profit

maximization problem is formulated for the

double renting scheme and the optimized

configuration of a cloud platform is obtained by

solving the profit maximization problem. Finally,

a series of calculations are conducted to

compare the profit of our proposed scheme with

that of the single renting scheme. The results

show that our scheme can not only guarantee the

service quality of all requests, but also obtain

more profit than the latter.

1. INTRODUCTION

Cloud computing is quickly becoming an

effective and efficient way of computing

resources. By centralized management of

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hosted services over the Internet. Cloud

computing is able to provide the most

cost-effective and energy-efficient way of computing

resources management. Cloud computing turn’s

information technology into ordinary

commodities and utilities by using the

pay-peruse pricing model. A service provider rents

resources from the infrastructure vendors, builds

appropriate multi server systems, and provides

various services to users. A consumer submits a

service request to a service provider, receives the

desired result from the service provider with

certain service-level agreement. Then pays for

the service based on the amount of the service

and the quality of the service. A service provider

can build different multi server systems for

different application domains, such that service

requests of different nature are sent to different

multi server systems. Owing to redundancy of

computer system networks and storage system

cloud may not be reliable for data, the security

score is concerned. In cloud computing security

is tremendously improved because of a superior

technology security system, which is now easily

available and affordable. Applications no longer

run on the desktop Personal Computer but run in

the cloud. This means that the PC does not need

the processing power or hard disk space as

demanded by traditional desktop software.

Powerful servers and the like are no longer

required. The computing power of the cloud can

be used to replace or supplement internal

computing resources. Organizations no longer

have to purchase computing resources to handle

the capacity peaks. Cloud computing is quickly

becoming an effective and efficient way of

computing resources. By centralized

management of resources and services, cloud

computing delivers hosted services over the

Internet. Cloud computing is able to provide the

most cost-effective and energy-efficient way of

computing resources management. Cloud

computing turn’s information technology into

ordinary commodities and utilities by using the

pay-per-use pricing model. A service provider

rents resources from the infrastructure vendors,

builds appropriate multi server systems, and

provides various services to users. A consumer

submits a service request to a service provider,

receives the desired result from the service

provider with certain service-level agreement.

Then pays for the service based on the amount of

the service and the quality of the service. A

service provider can build different multi server

systems for different application domains, such

that service requests of different nature are sent

to different multi server systems. Owing to

(3)

storage system cloud may not be reliable for

data, the security score is concerned. In cloud

computing security is tremendously improved

because of a superior security system, which is

now easily available and affordable. Applications

no longer run on the desktop Personal Computer

but run in the cloud. This means that the PC does

not need the processing power or hard disk space

as demanded by traditional desktop software.

Powerful servers and the like are no longer

required. The computing power of the cloud can

be used to replace or supplement internal

computing resources. Organizations no longer

have to purchase computing resources to handle

the capacity peaks.

2. LITERATURE SURVEY

Existing clouds focus on the provision of web

services targeted to developers, such as Amazon

Elastic Compute Cloud (EC2) [4], or the

deployment of servers, such as Go Grid [1].

Emerging clouds such as the Amazon Simple DB

and Simple Storage Service offer data

management services. Optimal pricing of cached

structures is central to maximizing profit for a

cloud that offers data services. Cloud businesses

may offer their services for free, such as Google

Apps [2] and Microsoft Azure [3] or based on a

pricing scheme. Amazon Web Service (AWS)

clouds include separate prices for infrastructure

elements, i.e. disk space, CPU, I/O and

bandwidth. Pricing schemes are static, and give

the option for pay as-you-go. Static pricing

cannot guarantee cloud profit maximization. The

cloud caching service can maximize its profit

using an optimal pricing scheme. This work

proposes a pricing scheme along the insight that

it is sufficient to use a simplified price-demand

model which can be re-evaluated in order to

adapt to model mismatches, external

disturbances and errors, employing feedback

from the real system behavior and performing

refinement of the optimization procedure.

Overall, optimal pricing necessitates an

appropriately simplified price-demand model

that incorporates the correlations of structures in

the cache services

3 PROFIT MAXIMIZATION IN CLOUD

COMPUTING

From this Paper We ReferredWe have proposed

a pricing model for cloud computing which takes

many factors into considerations, such as the

requirement r of a service, the workload of an

application environment, the configuration (m

and s) of a multi-server system, the service level

agreement c, the satisfaction (r and s) of a

(4)

penalty d of a low-quality service, the cost of

renting, the cost of energy consumption, and a

service provider’s margin and profit a. By using

an M/M/ m queuing model, we formulated and

solved the problem of optimal multiserver

configuration for profit maximization in a cloud

computing environment. Our discussion can be

easily extended to other service charge functions.

Our methodology can be applied to other pricing

models. At three-tier cloud structure, which

consists of infrastructure vendors, service

providers and consumers, the latter two parties

are particular interest to us. Clearly, scheduling

strategies in this scenario should satisfy the

objectives of both parties. Our contributions

include the development of a pricing model using

processor-sharing for clouds, the application of

this pricing model to composite services with

dependency consideration, and the development

of two sets of profit-driven scheduling

algorithms. From this Paper We ReferredA

pricing model is developed for cloud computing

which takes many factors into considerations,

such as the requirement r of a service, the

workload of an application environment, the

configuration (m and s) of a multi-server system,

the service level agreement c, the satisfaction (r

ands0) of a consumer, the quality (W and T) of a

service, the penalty d of a low-quality service,

the cost of renting, the cost of energy

consumption, and a service provider’s margin

and profit. And this will schedules the job

according to optimization of speed and size of the input hereby maximizing the profit. •

4. PROPOSED MECHANISM

In this section, we first propose the

Double-QualityGuaranteed (DQG) resource renting

scheme which combines long-term renting with

short-term renting. The IJARCCE ISSN (Online)

2278-1021 ISSN (Print) 2319 5940 International

Journal of Advanced Research in Computer and

Communication Engineering Vol. 4, Issue 12,

December 2015 Copyright to IJARCCE DOI

10.17148/IJARCCE.2015.41294 403 main

computing capacity is provided by the long-term

rented servers due to their low price. The

short-term rented servers provide the extra capacity in

peak period Advantages: In proposed system we

are using the Double-QualityGuaranteed (DQG)

renting scheme can achieve more profit than the

compared Single-Quality-Unguaranteed (SQU)

renting scheme in the premise of guaranteeing

the service quality completely. Cloud

Computing: Cloud computing describes a type of

outsourcing of computer services, similar to the

way in which the supply of electricity is

(5)

need to worry where the electricity is from, how

it is made, or transported. Every month, they pay

for what they consumed. The idea behind cloud

computing is similar: The user can simply use

storage, computing power, or specially crafted

development environments, without having to

worry how these work internally. Cloud

computing is usually Internet-based computing.

The cloud is a metaphor for the Internet based on

how the internet is described in computer

network diagrams; which means it is an

abstraction hiding the complex infrastructure of

the internet. It is a style of computing in which

IT-related capabilities are provided “as a

service”, allowing users to access

technology-enabled services from the Internet ("in the

cloud")without knowledge of, or control over the

technologies behind these servers. Queuing

model: We consider the cloud service platform as

a multiserver system with a service request

queue. The clouds provide resources for jobs in

the form of virtual machine (VM). In addition,

the users submit their jobs to the cloud in which

a job queuing system such as SGE, PBS, or

Condor is used. All jobs are scheduled by the job

scheduler and assigned to different VMs in a

centralized way. Hence, we can consider it as a

service request queue. For example, Condor is a

specialized workload management system for

compute intensive jobs and it provides a job

queuing mechanism, scheduling policy, priority

scheme, resource monitoring, and resource

management. Users submit their jobs to Condor,

and Condor places them into a queue, chooses

when and where to run them based upon a

policy. An M/M/m+D queuing model is build for

our multiserver system with varying system size.

And then, an optimal configuration problem of

profit maximization is formulated in which many

factors are taken into considerations, such as the

market demand, the workload of requests, the

server-level agreement, the rental cost of servers,

the cost of energy consumption, and so forth.

The optimal solutions are solved for two

different situations, which are the ideal optimal

solutions and the actual optimal solutions.

Business Service Providers Module: Service

providers pay infrastructure providers for renting

their physical resources, and charge customers

for processing their service requests, which

generates cost and revenue, respectively. The

profit is generated from the gap between the

revenue and the cost.In this module the service

providers considered as cloud brokers because

they can play an important role in between cloud

customers and infrastructure providers ,and he

can establish an indirect connection between

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Infrastructure Service Provider Module: In the

three-tier structure, an infrastructure provider the

basic hardware and software facilities. A service

provider rents resources from infrastructure

providers and prepares, a set of services in the

form of virtual machine (VM). Infrastructure

providers provide two kinds of resource renting

schemes, e.g., long-term renting and short-term

renting. In general, the rental price of long-term

renting is much cheaper than that of short-term

renting. Cloud Customers: A customer submits a

service request to a service provider which

delivers services on demand. The customer

receives the desired result from the service

provider with certain service-level agreement,

and pays for the service based on the amount of

the service and the service quality.

5. CONCLUSION

Maximize the profit of service providers, this

paper has proposed a novel

Double-Quality-Guaranteed (DQG) renting scheme for service

providers. This scheme combines short-term

renting with long-term renting, which can reduce

the resource waste greatly and adapt to the

dynamical demand of computing capacity. An

M/M/m+D queuing model is build for our

multiserver system with varying system size.

And then, an optimal configuration problem of

profit maximization is formulated in which many

factors are taken into considerations, such as the

market demand, the workload of requests, the

server- agreement, the rental cost of servers, the

cost of energy consumption, and so forth. The

optimal solutions are solved for two different

situations, which are the ideal optimal solutions

and the actual optimal solutions. In addition, a

series of calculations are conducted to compare

the profit obtained by the DQG renting scheme

with the Single-Quality-Unguaranteed (SQU)

renting scheme. The results show that our

scheme outperforms the SQU scheme in terms of

both of service quality and profit.

REFERENCES

[1]. The National Institute of Standards and

Technology (NIST) Sept2011.

[2] Saleforce.com, retrieved on 10 Sep. 2010,

http://www.salesforce.com/au/.

[3] SobirBazarbayev,” Content-Based

Scheduling of Virtual Machines (VMs) in the

Cloud” in University of Illinois at

Urbana-Champaign, AT&T Labs Research.

[4] J. Varia, Architecting applications for the

Amazon Cloud, in: R. Buyya, J. Broberg, A.

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ISBN: 978- 0470887998, 2010,

http://aws.amazon.com.

[5] Microsoft Azure retrieved on 10 Sep. 2010,

http://www.microsoft.com/windowsazure/.

[6]. SLA-based Resource Allocation for

Software as a Service Provider (SaaS) in cloud

Computing environments” in 2011 11th

[7] T. Gad, “Why Traditional Enterprise Software Sales Fail”. July 2010, Retrieved on 6th

Dec 2010:

http://www.sandhill.com/opinion/editorial_print.

php?id=307

[8]. Kiran Kumar et. al., “An Adaptive

Algorithm For Dynamic Priority Based Virtual

Machine Scheduling In Cloud” in IJCSI

International Journal of Computer Science

Issues, Vol. 9, Issue 6, No 2, November 2012.

[9]. Google App Engine,

http://appengine.google.com [22 Jan 2013]

[10]. Dr. Chenna Reddy , “An Efficient

Profit-based Job Scheduling Strategy for Service

Providers in Cloud Computing Systems” in

International Journal of Application or

Innovation in Engineering &Management

(IJAIEM) , Volume 2, Issue 1, January 2013.

[11]. Archana Pawar et al, ” A Review on

Virtual Machine Scheduling in Cloud

Computing” in International Journal of Computer

Science and Mobile Computing, Vol.3 Issue.4,

April- 2014, pg. 928-933

[12]. Linlin Wu, Saurabh Kumar Garg,

RajkumarBuyya, “SLA-based admission control

for a Software-as-a-Service provider in Cloud

computing environments” in Journal of

Computer and System Sciences

www.elsevier.com/locate/jcss 78 (2012) 1280–

1299.

[13] I. Popovici, and J. Wiles, “Proitable services in an uncertain world”.In Proceeding of the18th

Conference on Supercomputing (SC 2005),

Seattle, WA.

[14] M. Bichler, T. Setzer, Admission control for

media on demand services. Service oriented

computing and application, in: Proceedings of

IEEE International Conference on Service

Oriented Computing and Applications (SOCA

2007), Newport Beach, California, USA, 2007.

[15]. Pankesh Patel, AjithRanabahu, Amit Seth,

Service Level Agreement in Cloud Computing,

In OOPSLA 2009 Workshop.

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G.NEERAJA PG Scholar, Department of CSE, Vaagdevi College Of Engineering

Bollikunta,Warangal, Telangana Mail ID : neeraja.nvs07@gmail.com

K SHEKHAR.Assistant Professor Department of CSE, Vaagdevi College of Engineering,

References

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